CANCER PREDICTION USING NAIVE BAYES THEOREM

Cancer prediction plays a crucial role in improving patient outcomes by enabling early diagnosis and intervention. This study presents a novel approach to cancer prediction using the Naive Bayes algorithm, a well-established probabilistic classification technique. Leveraging a comprehensive dataset of cancer-related features, the algorithm models the relationships between features and cancer outcomes. The Naive Bayes model's simplicity and efficiency make it particularly suitable for medical diagnosis tasks. Through rigorous training, validation, and performance evaluation, the study demonstrates the algorithm's ability to accurately predict cancer occurrences. Insights into influential features aid medical professionals in understanding underlying factors. This research underscores the potential of Naive Bayes as a valuable tool in cancer prediction, paving the way for improved medical decision-making.